def create_estimator_spec(
self, features, mode, logits, labels=None, train_op_fn=None):
"""See `Head`."""
with ops.name_scope('head'):
logits = head_lib._check_logits(logits, self.logits_dimension) # pylint:disable=protected-access
# Predict.
pred_keys = prediction_keys.PredictionKeys
with ops.name_scope(None, 'predictions', (logits,)):
probabilities = math_ops.sigmoid(logits, name=pred_keys.PROBABILITIES)
predictions = {
pred_keys.LOGITS: logits,
pred_keys.PROBABILITIES: probabilities,
}
if mode == model_fn.ModeKeys.PREDICT:
return model_fn.EstimatorSpec(
mode=model_fn.ModeKeys.PREDICT,
predictions=predictions,
export_outputs={
'': export_output.ClassificationOutput(scores=probabilities)
})
# Eval.
unweighted_loss, processed_labels = self.create_loss(
features=features, mode=mode, logits=logits, labels=labels)
# Averages loss over classes.
per_example_loss = math_ops.reduce_mean(
unweighted_loss, axis=-1, keep_dims=True)
weights = head_lib._weights(features, self._weight_column) # pylint:disable=protected-access
training_loss = losses.compute_weighted_loss(
per_example_loss, weights=weights, reduction=losses.Reduction.SUM)
if mode == model_fn.ModeKeys.EVAL:
return model_fn.EstimatorSpec(
mode=model_fn.ModeKeys.EVAL,
predictions=predictions,
loss=training_loss,
eval_metric_ops=self._eval_metric_ops(
labels=processed_labels,
probabilities=probabilities,
weights=weights,
per_example_loss=per_example_loss))
# Train.
if train_op_fn is None:
raise ValueError('train_op_fn can not be None.')
with ops.name_scope(''):
summary.scalar(
head_lib._summary_key(self._name, metric_keys.MetricKeys.LOSS), # pylint:disable=protected-access
training_loss)
summary.scalar(
head_lib._summary_key( # pylint:disable=protected-access
self._name, metric_keys.MetricKeys.LOSS_MEAN),
losses.compute_weighted_loss(
unweighted_loss, weights=weights,
reduction=losses.Reduction.MEAN))
return model_fn.EstimatorSpec(
mode=model_fn.ModeKeys.TRAIN,
predictions=predictions,
loss=training_loss,
train_op=train_op_fn(training_loss))
def create_estimator_spec(self):
# Predict.
with ops.name_scope('head'):
with ops.name_scope(None, 'predictions', (self.logits_combine,)):
logits = head_lib._check_logits(self.logits_combine, self.logits_dimension)
logistic = math_ops.sigmoid(logits, name='logistic')
two_class_logits = array_ops.concat(
(array_ops.zeros_like(logits), logits), 1,
name='two_class_logits')
scores = nn.softmax(two_class_logits, name='probabilities')
class_ids = array_ops.reshape(
math_ops.argmax(two_class_logits, axis=1),
(-1, 1),
name='classes')
classes = string_ops.as_string(class_ids, name='str_classes')
predictions = {
'logits': logits,
'logistic': logistic,
'probabilities': scores,
'class_ids': class_ids,
'classes': classes,
}
if self.mode == model_fn.ModeKeys.PREDICT:
#batch_size = array_ops.shape(logistic)[0]
#export_class_list = string_ops.as_string([0, 1])
#export_output_classes = array_ops.tile(
# input=array_ops.expand_dims(input=export_class_list, axis=0),
# multiples=[batch_size, 1])
classifier_output = RankClassifierExportOutput(prob=scores)
return model_fn.EstimatorSpec(
mode=model_fn.ModeKeys.PREDICT,
predictions=predictions,
export_outputs={
'serving_default': classifier_output,
'classification': classifier_output,
'regression': export_output.RegressionOutput(logistic),
'predict': export_output.PredictOutput(predictions)
})
# calculate loss
unweighted_loss, processed_labels, training_loss, weights = self.calc_loss(logits)
# Eval.
if self.mode == model_fn.ModeKeys.EVAL:
eval_metric_ops = self.eval_metric_ops(
labels=processed_labels,
logits=logits,
logistic=logistic,
scores=scores,
class_ids=class_ids,
unweighted_loss=unweighted_loss,
weights=weights)
return model_fn.EstimatorSpec(
mode=model_fn.ModeKeys.EVAL,
predictions=predictions,
loss=training_loss,
eval_metric_ops=eval_metric_ops)
# Train
return self.train_fn(training_loss, unweighted_loss, weights, predictions)
def create_estimator_spec(self):
# Predict.
with ops.name_scope('head'):
with ops.name_scope(None, 'predictions', (self.logits_combine, )):
dnn_logits = head_lib._check_logits(self.logits_combine,
self.logits_dimension)
item_id_net = self.item_id_net
if (self.mode == model_fn.ModeKeys.EVAL):
logits = tf.reduce_sum(tf.multiply(dnn_logits,
item_id_net),
reduction_indices=1,
keep_dims=True)
#logits = tf.reduce_sum(tf.multiply(dnn_logits, item_id_net), reduction_indices=1, keep_dims = True) + tf.reduce_sum(self.bias_net, axis = 1, keep_dims = True)
else:
logits = tf.reduce_sum(
tf.multiply(dnn_logits, item_id_net),
reduction_indices=1,
keep_dims=True) + tf.reduce_sum(
self.bias_net, axis=1, keep_dims=True)
logistic = math_ops.sigmoid(logits, name='logistic')
two_class_logits = array_ops.concat(
(array_ops.zeros_like(logits), logits),
1,
name='two_class_logits')
scores = nn.softmax(two_class_logits, name='probabilities')
class_ids = array_ops.reshape(math_ops.argmax(two_class_logits,
axis=1), (-1, 1),
name='classes')
classes = string_ops.as_string(class_ids, name='str_classes')
predictions = {
'logits': logits,
'logistic': logistic,
'probabilities': scores,
'class_ids': class_ids,
'classes': classes,
}
# calculate loss
unweighted_loss, processed_labels, training_loss, weights = self.calc_loss(
logits)
# Eval.
if self.mode == model_fn.ModeKeys.EVAL:
return model_fn.EstimatorSpec(mode=model_fn.ModeKeys.EVAL,
predictions=predictions,
loss=training_loss)
# Train
return self.train_fn(training_loss, unweighted_loss, weights,
predictions)
def create_estimator_spec(self,
features,
mode,
logits,
labels=None,
train_op_fn=None):
"""See `Head`."""
with ops.name_scope(self._name, 'head'):
logits = head_lib._check_logits(logits, self.logits_dimension) # pylint:disable=protected-access
# Predict.
pred_keys = prediction_keys.PredictionKeys
with ops.name_scope(None, 'predictions', (logits, )):
probabilities = math_ops.sigmoid(logits,
name=pred_keys.PROBABILITIES)
predictions = {
pred_keys.LOGITS: logits,
pred_keys.PROBABILITIES: probabilities,
}
if mode == model_fn.ModeKeys.PREDICT:
classifier_output = head_lib._classification_output( # pylint:disable=protected-access
scores=probabilities,
n_classes=self._n_classes,
label_vocabulary=self._label_vocabulary)
return model_fn.EstimatorSpec(
mode=model_fn.ModeKeys.PREDICT,
predictions=predictions,
export_outputs={
_DEFAULT_SERVING_KEY:
classifier_output,
head_lib._CLASSIFY_SERVING_KEY:
classifier_output, # pylint:disable=protected-access
head_lib._PREDICT_SERVING_KEY: ( # pylint:disable=protected-access
export_output.PredictOutput(predictions))
})
(weighted_sum_loss, example_weight_sum,
processed_labels) = self.create_loss(features=features,
mode=mode,
logits=logits,
labels=labels)
# Eval.
if mode == model_fn.ModeKeys.EVAL:
return model_fn.EstimatorSpec(
mode=model_fn.ModeKeys.EVAL,
predictions=predictions,
loss=weighted_sum_loss,
eval_metric_ops=self._eval_metric_ops(
labels=processed_labels,
probabilities=probabilities,
weights=head_lib._weights(features,
self._weight_column), # pylint:disable=protected-access,
weighted_sum_loss=weighted_sum_loss,
example_weight_sum=example_weight_sum))
# Train.
if train_op_fn is None:
raise ValueError('train_op_fn can not be None.')
with ops.name_scope(''):
summary.scalar(
head_lib._summary_key(self._name, metric_keys.MetricKeys.LOSS), # pylint:disable=protected-access
weighted_sum_loss)
summary.scalar(
head_lib._summary_key( # pylint:disable=protected-access
self._name, metric_keys.MetricKeys.LOSS_MEAN),
weighted_sum_loss / example_weight_sum)
return model_fn.EstimatorSpec(mode=model_fn.ModeKeys.TRAIN,
predictions=predictions,
loss=weighted_sum_loss,
train_op=train_op_fn(weighted_sum_loss))
def create_estimator_spec(
self, features, mode, logits, labels=None, train_op_fn=None):
"""See `Head`."""
with ops.name_scope(self._name, 'head'):
logits = head_lib._check_logits(logits, self.logits_dimension) # pylint:disable=protected-access
# Predict.
pred_keys = prediction_keys.PredictionKeys
with ops.name_scope(None, 'predictions', (logits,)):
probabilities = math_ops.sigmoid(logits, name=pred_keys.PROBABILITIES)
predictions = {
pred_keys.LOGITS: logits,
pred_keys.PROBABILITIES: probabilities,
}
if mode == model_fn.ModeKeys.PREDICT:
classifier_output = head_lib._classification_output( # pylint:disable=protected-access
scores=probabilities, n_classes=self._n_classes,
label_vocabulary=self._label_vocabulary)
return model_fn.EstimatorSpec(
mode=model_fn.ModeKeys.PREDICT,
predictions=predictions,
export_outputs={
_DEFAULT_SERVING_KEY: classifier_output,
head_lib._CLASSIFY_SERVING_KEY: classifier_output, # pylint:disable=protected-access
head_lib._PREDICT_SERVING_KEY: ( # pylint:disable=protected-access
export_output.PredictOutput(predictions))
})
(weighted_sum_loss, example_weight_sum,
processed_labels) = self.create_loss(
features=features, mode=mode, logits=logits, labels=labels)
# Eval.
if mode == model_fn.ModeKeys.EVAL:
return model_fn.EstimatorSpec(
mode=model_fn.ModeKeys.EVAL,
predictions=predictions,
loss=weighted_sum_loss,
eval_metric_ops=self._eval_metric_ops(
labels=processed_labels,
probabilities=probabilities,
weights=head_lib._weights(features, self._weight_column), # pylint:disable=protected-access,
weighted_sum_loss=weighted_sum_loss,
example_weight_sum=example_weight_sum))
# Train.
if train_op_fn is None:
raise ValueError('train_op_fn can not be None.')
with ops.name_scope(''):
summary.scalar(
head_lib._summary_key(self._name, metric_keys.MetricKeys.LOSS), # pylint:disable=protected-access
weighted_sum_loss)
summary.scalar(
head_lib._summary_key( # pylint:disable=protected-access
self._name, metric_keys.MetricKeys.LOSS_MEAN),
weighted_sum_loss / example_weight_sum)
return model_fn.EstimatorSpec(
mode=model_fn.ModeKeys.TRAIN,
predictions=predictions,
loss=weighted_sum_loss,
train_op=train_op_fn(weighted_sum_loss))